A quiet programming revolution is unfolding in the world of code. While traditional AI still solves problems through brute force, a self-evolving agent called SE-Agent has learned to think like biological evolution. Every line of code is self-optimized, and each attempt accumulates wisdom.
This innovative framework, jointly incubated by top institutions such as the Chinese Academy of Sciences, Tsinghua University, and Step Star, is redefining artificial intelligence's programming capabilities in an unprecedented way. SE-Agent not only successfully broke through the programming bottlenecks of the Claude-4 model but also set remarkable SOTA records in the open-source community, injecting new vitality into the entire AI programming field.
Traditional AI agents are like isolated individuals, starting from scratch every time they solve a problem, repeating the same mistakes as if suffering from amnesia. This isolated approach leads to rigid thinking and often traps them in local optimal solutions. The emergence of SE-Agent has completely changed this situation. It integrates the essence of Darwin's theory of evolution into algorithm design, treating each solution path as a unique species and continuously evolving through mechanisms of natural selection and survival of the fittest.
The core charm of SE-Agent lies in its threefold evolutionary mechanism. The revision operation gives the agent deep introspection capabilities, allowing it to reflect on and improve each solution path, ensuring diversity at the start. The recombination operation breaks down barriers between different trajectories, promoting knowledge integration across disciplines, just like genetic recombination, enabling the agent to extract key elements from different paths and recombine them into more powerful solutions. The refinement operation acts as natural selection, strictly scoring new and old paths through a multi-dimensional evaluation system, eliminating the weak and retaining the strong, continuously iterating until the most robust answer is found.
Data never lies. In the latest SWE-Bench Verified benchmark test, known as the Olympics of programming, SE-Agent delivered an astonishing performance: the problem-solving success rate of the Claude-3.7-Sonnet model surged by 20.6%, and the success rate on the first attempt climbed to an impressive 61.2%, completely breaking the historical record in this field. These numbers represent not only a technical breakthrough but also a fundamental transformation in AI programming thinking.
Different from traditional methods that rely on brute-force search, SE-Agent demonstrates a higher level of intelligence. It is no longer a simple trial-and-error cycle but significantly reduces the number of iterations needed to reach the optimal solution through a structured evolutionary mechanism, achieving a dual improvement in efficiency and quality.
More excitingly, the self-evolutionary path pioneered by SE-Agent opens up new possibilities for enhancing complex reasoning abilities. It not only proves the great potential of collaborative work among agents but also points the way for the development of future general artificial intelligence. The team has already set their sights on broader application prospects, planning to expand this revolutionary self-evolutionary idea to more cutting-edge fields such as reinforcement learning and intelligent planning, helping to bring a stronger and more robust general artificial intelligence to life sooner.
SE-Agent's decision to open source is a gift to the global developer community. This means researchers and engineers around the world can continue to innovate on this powerful framework, collectively pushing the boundaries of AI programming capabilities forward.
While other AIs are still following instructions step by step, SE-Agent has learned to self-evolve. This is not just a technological advancement, but an important milestone in the history of artificial intelligence, signaling that we are entering a new era where agents can learn and improve independently.
Open Source Code: https://github.com/JARVIS-Xs/SE-Agent
